Image classification is a key application of computer vision with direct relevance to medical diagnostics, autonomous vehicles, and remote sensing. This paper discusses the use of an adaptive learning convolutional neural network (AL-CNN) for image classification, with results reported on a large-scale benchmark dataset that is widely accepted for performance evaluation. The AL-CNN architecture integrates convolutional, pooling, and fully connected layers. The model was systematically trained on a subset of the dataset and subsequently tested on an independent validation subset to evaluate its efficiency and generalization capability. In addition, optimization techniques such as data augmentation, dropout, and advanced activation functions were employed to further enhance model performance. The results, based on accuracy metrics, indicate the successful implementation of the proposed AL-CNN model for reliable and accurate image classification. This study demonstrates the potential of the AL-CNN approach to address various complexities in image classification, thereby enabling further innovation in this domain.
M. Chawla, Rashmi Agrawal, Bharat Bhushan· Bulletin of Electrical Engin...· 0 citations
Artificial intelligence (AI) is increasingly being incorporated into ambulatory and preventive pediatric practice through advances in machine learning, deep learning, computer vision, natural language processing, and mobile health technologies. The growing availability of electronic health records, wearable devices, telemedicine platforms, and digital health applications has created new opportunities for AI-assisted screening, risk prediction, preventive care, and outpatient clinical decision support. In pediatric healthcare, AI has emerging applications in developmental and autism screening, vision and hearing assessment, growth and nutrition monitoring, vaccination programs, infectious disease surveillance, symptom triage, prescribing support, remote patient monitoring, and parent-facing digital health tools. These technologies have the potential to improve early diagnosis, enhance preventive interventions, expand access to care, and support more personalized management of pediatric patients in outpatient settings. However, significant challenges remain, including limited pediatric-specific validation, algorithmic bias, data privacy concerns, regulatory uncertainties, workflow integration barriers, and the need to maintain trust among clinicians and caregivers. This narrative review summarizes current evidence regarding AI applications in ambulatory pediatrics, preventive pediatrics, and pediatric outpatient care, highlighting their clinical utility, limitations, implementation challenges, and future directions. This article is the second in a four-part series on AI in pediatrics; subsequent articles will discuss AI applications in pediatric inpatient and critical care settings, as well as the ethical, regulatory, and future implications of AI in child healthcare
Amit Agrawal, Rashmi Agrawal· The Indian journal of child...· 0 citations